Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0021051 (immunodeficiency)
 71,517 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

This article outlines the history and rationale of a multisite study of blood-borne infections among persons with severe mental illness reported in this special section of Psychiatric Services. The general problem of blood-borne diseases in the United States is reviewed, particularly as it affects people with severe mental illness and those with comorbid substance use disorders. The epidemiology and natural history of three of the most important infections are reviewed: the human immunodeficiency virus (HIV), the hepatitis B virus, and the hepatitis C virus. Current knowledge about blood-borne diseases among people with severe mental illness as well as information on current treatment advances for hepatitis C are summarized. A heuristic model, based on the pragmatic, empirical, and conceptual issues that influenced the final study design, is presented. The specific rationale of the five-site collaborative design is discussed, as well as the sampling frames, measures, and procedures used at the participating sites. Alternative strategies for analyzing data deriving from multisite studies that use nonrandomized designs are described and compared. Finally, each of the articles in this special section is briefly outlined, with reference to the overall hypotheses of the studies.
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PMID:The five-site health and risk study of blood-borne infections among persons with severe mental illness. 1277 96

Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few closely related systems has guided our understanding of the mechanisms by which changes such as ligand binding or mutation can alter the function. The general problem of detecting and interpreting such mechanisms from simulations of many related systems, however, remains a challenge. This problem is addressed here by applying supervised and unsupervised machine learning techniques to a variety of thermodynamic observables extracted from molecular dynamics simulations of different systems. As an important test case, these methods are applied to understand the evasion by human immunodeficiency virus type-1 (HIV-1) protease of darunavir, a potent inhibitor to which resistance can develop via the simultaneous mutation of multiple amino acids. Complex mutational patterns have been observed among resistant strains, presenting a challenge to developing a mechanistic picture of resistance in the protease. In order to dissect these patterns and gain mechanistic insight into the role of specific mutations, molecular dynamics simulations were carried out on a collection of HIV-1 protease variants, chosen to include highly resistant strains and susceptible controls, in complex with darunavir. Using a machine learning approach that takes advantage of the hierarchical nature in the relationships among the sequence, structure, and function, an integrative analysis of these trajectories reveals key details of the resistance mechanism, including changes in the protein structure, hydrogen bonding, and protein-ligand contacts.
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PMID:Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease. 3187 49